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126 A New Approach to Accident Analysis: Multiple Agent Perception-Action Zobair Ibn Awal 1 and Kazuhiko Hasegawa 2 1. Doctoral Student, Graduate School of Engineering, Osaka University, Japan 2. Professor, Graduate School of Engineering, Osaka University, Japan The economic and social impact of maritime accidents are enormous and devastating. In recent times the world experienced some grievous accidents which put serious challenges to the existing methods of safety evaluation. Over the years many research has been conducted on risk analysis and improvement of safety standards. Yet accidents are taking place and human elements are the major contributing factors. This paper proposes a new technique based on logic programming (e.g. Prolog) method. It is considered that an accident is an unwanted event which initiates from hidden causes (e.g. various action(s)/perception(s) of ship crew). It is, therefore, discussed that using intelligent agents for evaluation of the actions/perceptions of ship crew may result in uncovering of the hidden root causes behind an accident. Intelligent agents are essentially computer programs which acts or behaves rationally according their percepts. The perception and action sequence of an intelligent agent depends on the given environment and knowledge base. Study reveals that such a technique may assist ship crew in evaluating their decisions for making a safe voyage. The merits and demerits of the method are discussed briefly and future recommendations are made. KEY WORDS: Maritime Accidents, Multiple Agent, Perception-Action, Logic Programming. INTRODUCTION According to International Maritime Organization (IMO) report, around 90% of world trade is carried by the international shipping (IMO, 2012). Without shipping the import and export of goods on the scale necessary for the modern world would not be possible. Interestingly, the shipping is estimated to be done by 1.5 million seafarer from almost all nations worldwide. This number of seaman is as much as (or perhaps greater than) the total population of small Europeans countries such as Estonia or Cyprus (Wikipedia, 2015a). Therefore, the safety of shipping that includes the safety of ship crew, the ship itself, the environment and others is a major concern for the society. However, recent maritime disasters such as MV Costa Concordia accident in 2012 (Wikipedia, 2015b) and MV Sewol accident in 2014 (Wikipedia, 2015c) have raised terrifying worries within the maritime community. The fundamental issue that concerns all that the state of the art ships and well trained dedicated ship crew are often unable avoid accidents. It is important to mention that in this study an accident is considered as an event of destruction of lives and resources where no criminal activity is involved. That is an unintentional event which was unforseable and unavoidable. The hidden faults within the system and/or procedures are to blame rather than an individual and it is essential to develop techniques which can identify these hidden faults. The quest for a better technique of safety evaluation is primary focus for many research groups. In this view, this paper attempts to present a new accident analysis method based on logic programming technique (LPT). The study includes a literature review on accident theories which discusses the fundamental aspects of accident causation. Afterwards, the paper presents the basic concepts LPT for model development. The results obtained from the model run is presented and discussed later. The concluding remarks are given based on the current state of the research and the future prospects. LITERATURE REVIEW The literature review of this paper includes several segments. At first the definitions of accident, accident analysis and accident model is explored. The development of accident theories and their chronological order of appearance are studied. The literature review suggested that the accident models are evolving over the past few decades and developments are ongoing. It has been observed that these accident models attribute many limitations where prospects for further developments are wonderful. The need for introducing new methods and techniques is also realized in this section. Accident, Accident Analysis and Accident Model The domain of accident analysis is comparatively young considering other disciplines of science and engineering. During the past one hundred years or so researchers have become interested in accident modelling. However, one of the earliest definition of accident was given by Heinrich in 1931 which has been referenced by Ward (2012). The definition is “An accident is an unplanned and uncontrolled event in which the action or reaction of an object, substance, person, or radiation results in personal injury or the probability thereof”. However, one may derive a simpler definition out of it - an accident is an unforeseen and un-planned event or circumstance that causes damage and/or injury. According to Stringfellow (2010) accident analysis is the process by which the reasons for the occurrence of an accident are uncovered. Information and lessons learnt from accident analysis are used to re-engineer the same or other systems so
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126

A New Approach to Accident Analysis:Multiple Agent Perception-Action

Zobair Ibn Awal1 and Kazuhiko Hasegawa2

1. Doctoral Student, Graduate School of Engineering, Osaka University, Japan2. Professor, Graduate School of Engineering, Osaka University, Japan

The economic and social impact of maritime accidents are enormous and devastating. In recent times the world experienced some grievous accidents which put serious challenges to the existing methods of safety evaluation. Over the years many research has been conducted on risk analysis and improvement of safety standards. Yet accidents are taking place and human elements are the major contributing factors. This paper proposes a new technique based on logic programming (e.g. Prolog) method. It is considered that an accident is an unwanted event which initiates from hidden causes (e.g. various action(s)/perception(s) of ship crew). It is, therefore, discussed that using intelligent agents for evaluation of the actions/perceptions of ship crew may result in uncovering of the hidden root causes behind an accident. Intelligent agents are essentially computer programs which acts or behaves rationally according their percepts. The perception and action sequence of an intelligent agent depends on the given environment and knowledge base. Study reveals that such a technique may assist ship crew in evaluating their decisions for making a safe voyage. The merits and demerits of the method are discussed briefly and future recommendations are made.

KEY WORDS: Maritime Accidents, Multiple Agent, Perception-Action, Logic Programming.

INTRODUCTIONAccording to International Maritime Organization (IMO) report,around 90% of world trade is carried by the international shipping (IMO, 2012). Without shipping the import and export of goods on the scale necessary for the modern world would not be possible. Interestingly, the shipping is estimated to be doneby 1.5 million seafarer from almost all nations worldwide. This number of seaman is as much as (or perhaps greater than) the total population of small Europeans countries such as Estonia or Cyprus (Wikipedia, 2015a). Therefore, the safety of shipping that includes the safety of ship crew, the ship itself, the environment and others is a major concern for the society.

However, recent maritime disasters such as MV Costa Concordia accident in 2012 (Wikipedia, 2015b) and MV Sewol accident in 2014 (Wikipedia, 2015c) have raised terrifyingworries within the maritime community. The fundamental issue that concerns all that the state of the art ships and well trained dedicated ship crew are often unable avoid accidents. It is important to mention that in this study an accident is considered as an event of destruction of lives and resources where no criminal activity is involved. That is an unintentional event which was unforseable and unavoidable. The hidden faults within the system and/or procedures are to blame rather than an individual and it is essential to develop techniques which can identify these hidden faults.

The quest for a better technique of safety evaluation is primary focus for many research groups. In this view, this paper attempts to present a new accident analysis method based on logic programming technique (LPT). The study includes a literature review on accident theories which discusses the fundamental aspects of accident causation. Afterwards, the paper presents the

basic concepts LPT for model development. The results obtained from the model run is presented and discussed later. The concluding remarks are given based on the current state of the research and the future prospects.

LITERATURE REVIEWThe literature review of this paper includes several segments. At first the definitions of accident, accident analysis and accident model is explored. The development of accident theories and their chronological order of appearance are studied. The literature review suggested that the accident models are evolving over the past few decades and developments are ongoing. It has been observed that these accident models attribute many limitations where prospects for further developments arewonderful. The need for introducing new methods and techniques is also realized in this section.

Accident, Accident Analysis and Accident ModelThe domain of accident analysis is comparatively young considering other disciplines of science and engineering. During the past one hundred years or so researchers have become interested in accident modelling. However, one of the earliest definition of accident was given by Heinrich in 1931 which hasbeen referenced by Ward (2012). The definition is “An accident is an unplanned and uncontrolled event in which the action or reaction of an object, substance, person, or radiation results in personal injury or the probability thereof”. However, one may derive a simpler definition out of it - an accident is an unforeseen and un-planned event or circumstance that causes damage and/or injury.

According to Stringfellow (2010) accident analysis is the process by which the reasons for the occurrence of an accident are uncovered. Information and lessons learnt from accident analysis are used to re-engineer the same or other systems so

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Awal A New Approach to Accident Analysis: Multiple Agent Perception-Action 2

that future accidents (which may or may not be the form) do not occur.

Typically, an accident model provides a conceptualization of the characteristics of the accident that normally shows the relation between causes and effects (Qureshi 2008). Since, an accident event is the result of some cause or causes, therefore, the challenge for accident analyst is to identify the relationshipbetween these causes and effects within the system.

An accident model or accident theory provides a hypothesis of accident causation and attempts to validate the hypothesisthrough extensive investigation. However, to validate thesetheories, there are several tools for accident analysis that essentially does not propose any hypothesis rather provide theoretical instruments for analyzing accidents. Fault Tree Analysis (FTA) (Vesely, Goldberg, Roberts and Haasl 1981), AcciMap (Rasmussen and Svedung 2000), and Coloured Petri nets (Vernez, Buchs and Pierrehumbert 2003) are just a few mentionable examples. These tools also allow investigators to explain the causation of accidents and assist in prevention of disasters.

Development of Accident TheoriesTraditional approach towards accident analysis, maritime accidents in particular, is using statistical tools to study the probability of accident causation with respect to different uncontrollable variables such as weather, geographical features etc. (e.g. Awal 2007; Awal, Islam and Hoque 2010). However,from a general perspective, many accident theories are being proposed over the years by many researchers which are able to explain maritime disasters and other accidents as well.

The literature review reveals that over the past few decades many accident theories and accident analysis tools have been proposed and developed. Some theories survived and some did not. This fact suggest that the interaction between man and machine is continuously changing and so are the causation of accidents. It is interesting to note that different branches of knowledge (such as ergonomics and human factors, organization theory, industrial psychology, medicine, environmental sciences, law etc.) can be utilized to explain accident phenomena. From the accident causation perspective, these fields are overlapping and originate complexities. Therefore, the accident analysis techniques vary widely. Khanzode, Maiti and Ray (2012) and Qureshi (2008) reviewed accident/injury theories and made respective classifications. For example, Khanzode, Maiti and Ray (2012) classified the accident theoriesas follows:

• 1st Generation: Accident proneness based• 2nd Generation: Domino theory based• 3rd Generation: Injury epidemiology based• 4th Generation: System based

The study by Qureshi (2007) reveals another type of classification of accident models. Such as:

• Traditional approaches to accident modelling (sequential models)

• Epidemiological/Organizational models of accident causation

• Systemic accident modelsA study by Awal and Hasegawa (2015) explored the chronological order of development and classification of accident theories all together, as shown in Figure 1. The study depicts an overall picture of the historical appearance and their characteristics in single form. It is evident that in recent time more complex system theoretic models are proposed compared to earlier sequential/epidemiological models.

Year

Acci

dent

The

orie

s

SequentialAccidentModels

1930 1935 1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

EpidemiologicalModels

SystemicModels

Perrow’s Normal Accident Theory (Perrow 1984)

Rasmussen’s Socio-technical Framework (Rasmussen 1997)

System Theoretic Accident Model and Process

(STAMP) (Leveson 2004)

Functional Resonance and Accident Model (Hollnagel

2004)

Haddon Matrix (Haddon1972, 1983)

Reason’s Organisational Accident Model, SwissCheese Model (Reason

1990, 1997)

Domino theory, Heinrich’s Law and Axiom’s of industrial Safety (Heinrich 1931)

Multi-linear Event sequencing Model (MES) (Benner 1975)

Figure 1. Development of accident theories in chronological order (Awal and Hasegawa, 2015).

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Most of the modern day accident models adopt the fact that accident takes place in a complex sociotechnical system in order to combine the social and technical attributes in the analysis (Qureshi 2008; and Khanzode, Maiti and Ray 2012). Most models are subjective by nature and requires extensive brainstorming for producing applicable results. So far very little computational techniques have been developed that can efficiently analyze accidents in an established programming domain. Such technique is believed to improve operational safety and extend the capacity of an accident analyst as well.Recent studies by Awal & Hasegawa (2014a, b) and Hasegawa & Awal (2013) describes the need for and progress of such anapproach. Research works reveal that the potentials of utilizing logic programming technique in accident analysis is tremendous.

Conclusion of Literature ReviewThe development of accident theories can be related to the change in sociotechnical context over the years. The rapid industrialization, change in interaction between men and machine is giving birth to new types of accidents. Therefore, new generation of accident analysis techniques are required to be introduced. It is also essential to extend the capacity of accident analyst with the help of powerful computational techniques and devices.

MODEL DEVELOPMENTIn this section the hypothesis of the accident analysis technique is described. The fundamental issues such as definition of logic,agents and theirs characteristics are described in order.

Hypothesis of the ModelThe hypothesis adopted in this study is that Logic Programming Technique (LPT) can be used to analyze and deduce the perception/action of human agents using deductive logic along with simulation of the concerned system in order to find out the unknown causes of a particular type of accident.

Definition of LogicLogic may be defined as the science of reasoning. Reasoning is a special mental activity called inferring, what can also be called making (or performing) inferences. A useful and simple definition of the word ‘infer’ may be given as 'To infer is to draw conclusions from premises'. In order to simplify the understanding of reasoning, logic treats both premises and conclusions in a single term called 'statements'. Logic correspondingly treats inferences in terms of collections of statements, which are also called 'arguments'. The definition of 'argument' that is relevant to logic is given as - 'an argument is a collection of statements, one of which is designated as the conclusion, and the remainder of which are designated as the premises'. Therefore, the reasoning process may be thought of as beginning with input (premises, data, etc.) and producing output (conclusions).

Agent: Definition and TypesAn agent can be anything that can be viewed as perceiving its environment through sensors and acting upon that environment

through actuators (Russel and Norvig 2010). For example, a software agent receives keystrokes, file contents and network packets as sensory inputs and acts on the environment bydisplaying on the screen, writing files, and sending network packets. In general, for an agent, choice of action at any given instant may depend on the entire percept sequence observed to date but not on anything that it has not perceived. Mathematically, an agent’s behavior is described by the agent function that maps and given percept sequence to an action.According to Russel and Norvig (2010) there are several types of agents with different characteristics:

• Simple reflex agent• Model-based reflex agent• Goal-based agent• Utility-based agent• Learning agent

In this study, simple reflex agents are considered for discussing the logic programming technique.

Design of an AgentThe characteristic of a simple reflex agent is that such an agent selects action(s) based on the current percept, ignoring the rest of the percept history. The agent uses the condition-action rule or situation-action rule. The simple reflex agent needs to have a library of rules so that if a certain situation should arise and it is in the set of condition-action rules the agent will know how to react with minimal reasoning. A schematic diagram of simple reflex agent is shown in Figure 2. An example of simple reflex agent could be the reaction of a person to fire. A person pulls his or her hand away without thinking about any possibility that there could be danger in the path of his/her arm. This is called reflex action. Similar to a person’s reaction to fire, a simple reflex agent behaves relative to the situation and does not consider previous percept.

Figure 2. A schematic diagram of simple reflex agent (Russel and Norvig 2010).

Ship Crew as AgentsAn example of ship crew in an organogram for a hypothetical ship is shown in Figure 3. There are two departments of crew such as the deck side and the engine side. The deck side crew is responsible for navigation, watch keeping etc and the engine side crew are responsible for propulsion, power generation and etc. It is important to comprehend that for a safe and optimum operation of a ship, communication among the ship crew is absolutely vital. In this study this communication is considered

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Awal A New Approach to Accident Analysis: Multiple Agent Perception-Action 4

in the form of perception-action cycle. For instance, during a voyage each crew is assigned some responsibility according to their qualification and designation. The chief engineer is responsible for maintaining the required power as needed and commanded by the Captain of the ship. Therefore, the communication between these two are vital when there is engine problem involved. A wrong perception from the Captain may result in a wrong command and the Chief Engineer may execute that wrong command without hesitation. This is also true in the opposite way as well. However, when all the crew are involved in this perception-action cycle, the scenario becomes very complicated for human comprehension. One of the main focus of this study is to identify the faults in this complex human perception-action cycle using logic computations.

Captain

Helmsman

Senior Officer of the Watch

Junior Officer of the Watch

ChiefEngineer

Engine Side Deck Side

SecondEngineer

WatchkeepingEngineer

Figure 3: An example of ship crew in an organogram for a hypothetical ship.

In this context an initial yet most significant step for agent design is to specify the task environment as fully as possible. Task environments are essentially the ‘problems’ to which the rational agents are the ‘solutions’ (Russel and Norvig 2010). The general practice for designing agents is to define or describe PEAS (Performance, Environment, Actuators and Sensors) as fully as possible. In this study, several agents are considered

based on the maritime context. Table 1 depicts a description of the agents in terms of PEAS. In this table, six simple reflex agents are shown as an example; including the ship itself and five ship crewmembers, such as a Captain, a Senior Officer of the Watch (SOOW), a Junior Officer of the Watch (JOOW), a Helmsman and a Chief Engineer. The following sections briefly describe the properties of these agents.

Ship AgentA ship agent is a mathematical model of ship maneuvering. In this study ship is considered as a simple reflex agent because the ship behaves according to its given commands and does not behave based on its behavior history. For example, the ship receives the rudder command given by helmsman and using this rudder command the ship agent computes its next position in the water, considering the speed, heading and turning rates are initially given. The ship will always compute its next position based on the given inputs and will not consider the new position based on old input values. Thereby the ship agent behaves like asimple reflex agent. Figure 4 shows the definition of ship agent.

The mathematical model for ship response to rudder commands is determined by Nomoto’s linear K-T model (Tzeng and Chen 1999; Journée and Pinkster 2002). The cardinal equations are given as follows:

𝑇𝑇𝑇𝑇�̈�𝜓𝜓𝜓 + �̇�𝜓𝜓𝜓 = 𝐾𝐾𝐾𝐾𝛿𝛿𝛿𝛿𝑟𝑟𝑟𝑟 (1)

Where,𝜓𝜓𝜓𝜓 = 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑟𝑟𝑟𝑟𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝐶𝐶𝐶𝐶𝛿𝛿𝛿𝛿𝑟𝑟𝑟𝑟 = 𝑅𝑅𝑅𝑅𝐶𝐶𝐶𝐶𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝐶𝐶𝐶𝐶𝑟𝑟𝑟𝑟 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝐶𝐶𝐶𝐶

𝑇𝑇𝑇𝑇 = �́�𝑇𝑇𝑇𝑈𝑈𝑈𝑈0

𝐿𝐿𝐿𝐿𝐾𝐾𝐾𝐾 = �́�𝐾𝐾𝐾

𝑈𝑈𝑈𝑈0

𝐿𝐿𝐿𝐿𝑈𝑈𝑈𝑈0 = 𝐼𝐼𝐼𝐼𝑎𝑎𝑎𝑎𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑓𝑓𝑓𝑓𝐶𝐶𝐶𝐶𝑟𝑟𝑟𝑟𝑓𝑓𝑓𝑓𝑎𝑎𝑎𝑎𝑟𝑟𝑟𝑟𝑅𝑅𝑅𝑅 𝐶𝐶𝐶𝐶𝑠𝑠𝑠𝑠𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑅𝑅𝑅𝑅𝐿𝐿𝐿𝐿 = 𝑆𝑆𝑆𝑆ℎ𝐼𝐼𝐼𝐼𝑠𝑠𝑠𝑠 𝑎𝑎𝑎𝑎𝐶𝐶𝐶𝐶𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝐼𝐼𝐼𝐼ℎ�́�𝑇𝑇𝑇 & �́�𝐾𝐾𝐾 𝑎𝑎𝑎𝑎𝑟𝑟𝑟𝑟𝐶𝐶𝐶𝐶 𝑎𝑎𝑎𝑎𝐶𝐶𝐶𝐶𝑎𝑎𝑎𝑎 𝑅𝑅𝑅𝑅𝐼𝐼𝐼𝐼𝑑𝑑𝑑𝑑𝐶𝐶𝐶𝐶𝑎𝑎𝑎𝑎𝐶𝐶𝐶𝐶𝐼𝐼𝐼𝐼𝐶𝐶𝐶𝐶𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑑𝑑𝑑𝑑𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑚𝑚𝑚𝑚𝐶𝐶𝐶𝐶𝑟𝑟𝑟𝑟𝐼𝐼𝐼𝐼𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑐𝑐𝑐𝑐𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝐼𝐼𝐼𝐼𝑐𝑐𝑐𝑐𝐼𝐼𝐼𝐼𝐶𝐶𝐶𝐶𝑎𝑎𝑎𝑎𝐼𝐼𝐼𝐼𝐶𝐶𝐶𝐶

Table 1. Example of PEAS definition of different agents.Name of Agent Performance Environment Actuator Sensor

Ship Calculate ship position and heading, evaluate status (sailing, grounded, etc.)

Coastal waterUnderwater rocks

Rudder angle and speed

Rudder command and Speed command

Captain Visual observation inside and outside the ship, listen to ship crew, Command to ship crew Bridge deck Verbal command and

manual operation Vision and hearing

SOOW Visual observation inside and outside the ship, communicate with ship crew. Bridge deck Verbal command and

manual operation Vision and hearing

JOOW Visual observation inside and outside the ship, communicate with ship crew and monitor route. Bridge deck Exchange information

and manual operation Vision and hearing

HelmsmanVisual observation inside and outside the ship, communicate with ship crew and execute command from Captain at the helm.

Bridge deck Exchange information and manual operation Vision and hearing

Chief EngineerVisual observation inside the engine room, communicate with ship crew and command engine room crew.

Engine Room Verbal command and manual operation Vision and hearing

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EnvironmentAgent: Ship

Water

Helm, BridgeDeckPercepts

Actions

What is the rudder

command?

Determine ships position.

Set rudder accordingly.

If-then rule for rudder.

Mathematicalmodel

Figure 4. Definition of ship agent.

Captain AgentThe captain of a ship is responsible for every action and its consequences that occur on-board. The Captain must control all the crew and the ship itself. In this study, the captain agent perceives the actions of ship crew and the action of the ship agent itself. Based on this perceptions and simple if-then rules the captain agent takes actions. Actions usually involve giving commands to other crew and manual operations such as controlling the engine rpm. The captain agent necessarily requires to have a set of situation-action rules based on which the agent can perceive and take action. These rules may be derived from the existing regulations and practices. Figure 5defines the captain agent.

SOOW AgentIn a ship, the senior officer of the watch needs to follow the tasks assigned by the Captain. For example, in the case of MV Costa Concordia, the SOOW was assigned to conduct ship maneuvering and route monitoring at different times during its voyage. In this study, the SOOW agent works under the captain and his working environment is inside the bridge deck. The agent perceives from the actions of other ship crew and visual observation from bridge deck gadgets. He may order the JOOW and conduct manual operations (e.g. route planning). Figure 6defines SOOW agent.

JOOW AgentIn a ship, the Junior Officer of the Watch (JOOW) usually works under the Captain and the SOOW and executes the orders of his or her superiors. For ex-ample, the JOOW may conduct route monitoring on the paper chart during a voyage or may execute any other command given by the Captain. In this study, the JOOW agent can perceive from the orders and actions from the ship crew. His own actions will be executing the orders from his superiors and ordering to his juniors. He may perceive from the surrounding world as well. Figure 7 defines the JOOW agent.

Based on the above mentioned agents it is however, possible todeduce the occurrences of events in chronological order. The following section briefly describes the logical deduction of accident by multiple agent perception-action.

EnvironmentAgent: Captain

Ship Crew,Bridge Deck,

Water

Ship Crew,Bridge Deck,

WaterPercepts

Actions

What is the world right now?

Action to be doneIf-then rule.

Figure 5. Definition of captain agent.

EnvironmentAgent: SOOW

Ship Crew

Ship Crew,Navigation DeskPercepts

Actions

What is the world right now?

Action to be doneIf-then rule.

Figure 6. Definition of SOOW agent.

EnvironmentAgent: JOOW

At the Helm, ShipCrew

At the Helm, ShipCrewPercepts

Actions

What is the world right now?

Action to be doneIf-then rule.

Figure 7. Definition of JOOW agent.

RESULTS AND DISCUSSIONSThis section describes the results obtained by model run. One of the principal objectives of this study is to demonstrate the potentials of logic computation along with numerical simulation in the same programming domain. Therefore, at first, the assumptions are discussed briefly. The knowledge of the human agents are discussed in tabular form where the arguments arepresented. Each argument is presented using with one premise (with P bullet) and one conclusion (with C bullet). In this particular study the agents are given very limited knowledge of perceptions and actions.

Scenario AssumptionsIn this study a simplified scenario is considered such as the following:

• Action-perception cycle of three crew members are studied in this simulation: (1) Captain, (2) Senior Officer of the Watch (SOOW) and (3) Junior Officer of the Watch (JOOW)

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• The ship’s original starting position in space is considered as (0, 0) where the vertical axis represents advance distance of ship and horizontal axis represents transfer distance of ship.

• There is a zone of scattered rocks visible from 2000 m in clear daylight but not visible at night. If the ship enters that zone, grounding accident is assumed to take place.

• The scattered rocks are located at a coordinate of (0, 3000), that is vertically 3 kilometer away from the starting position.

• The captain agent of may see the scattered rocks at night from a distance of 500 meter or less.

Assumptions for Ship Maneuvering ModelFor the ship maneuvering motion, the transition phase between dead stop to full ahead speed is not considered. The initial conditions are given in Table 2.

Table 2. Assumptions for ship maneuvering model.No. Item Value Unit1. Initial position in X axis 0 Meter2. Initial position in Y axis 0 Meter3. Initial heading 0 Degree4. Initial yaw rate 0 Degree/second5. Initial rudder angle 0 Degree6. Steady state speed 3 Meter/second

7. Maneuvering indices K 0.005T 300 Second

Captain’s KnowledgeThe captain agent’s knowledge of perceptions are presented in Table 3. The knowledge is shown in terms of arguments where there are two parts: a premise and a conclusion. The actions of captain are shown in Table 4. Here the captain agent plays the role of overall command.

SOOW’s KnowledgeThe SOOW agent’s knowledge of perceptions and actions are presented in Table 5 and Table 6 respectively. The SOOW plays the role of route planning and monitoring on navigation charts.

Table 3. Captain’s perceptions.Logic No. Statements

1 P Conduct route planning on small scale chartC Ship is ready for voyage

2 P Conduct route planning on large scale chartC Ship is ready for voyage

3 P Declare danger aheadC Need to change heading

4 P Lift anchorC Anchor lifted

5 P Declare danger aheadC Danger ahead

Table 4. Captain’s actions.Logic No. Statements

1P Need to make a sail past

C Command SOOW to change voyage plan for sailpast

2 P Ship is ready for voyageC Command JOOW to lift anchor

3 P Anchor liftedC Command JOOW - Full Ahead

4 P Danger aheadC Command JOOW 10 degree starboard

Table 5. SOOW’s perceptions.Logic No. Statements

1 P Command SOOW to change voyage plan for sail past

C Need to change voyage plan for sail past

2 P Need to change voyage plan for sail pastC Need to conduct route planning

Table 6. SOOW’s actions.Logic No. Statements

1P Need to conduct route planningC Conduct route planning on small scale chart

2P Need to conduct route planningC Conduct route planning on large scale chart

3 P Danger aheadC Declare danger ahead

JOOW’s KnowledgeThe JOOW agent is responsible for executing the commands from his/her superior such as lifting the anchor, speed of the ship and executing rudder command. The JOOW agent’s knowledge of perceptions are presented in Table 7 and the knowledge of actions are shown in Table 8.

Table 7. JOOW’s perceptions.Logic No. Statements

1P Command JOOW to lift anchorC Need to lift anchor

2P Command JOOW - Full AheadC Need to execute command - Full Ahead

3 P Command JOOW 10 degree starboardC Need to execute 10 degree starboard

Model Run and DiscussionBased on the above mentioned assumptions and scenario settings the model is constructed and executed in Prolog environment. The objective is to find out which decision made by the crew may result in a possible accident. A scenario is considered as shown in Figure 8 where at a voyage begins at

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Awal A New Approach to Accident Analysis: Multiple Agent Perception-Action 7

night. The voyage had an original route planned but the route is required to be changed due to some reason. The reason is beyond the scope of this study. Figure 8 shows the path ship for of two cases where in one case the SOOW decided to use small scale chart and in the other case the large scale chart. The characteristics of these two charts are such that the small scale chart shows some scattered rocks and the large scale chart doesn’t show the scattered rocks.

Table 8. JOOW’s actions.Logic No. Statements

1P Need to execute 10 degree starboardC Execute 10 degree starboard

2P Need to lift anchorC Lift anchor

3 P Need to execute command - Full AheadC Execute command - Full Ahead

The logical deductions derived from the perception-action of agents are shown iteratively in Table 9 and Table 10. It is evident from Figure 8 that the ship following small scale chart easily avoids the scattered rocky zone. The logical deduction shown in Table 9 reveals the reason. In small scale charts the rocky region is clearly marked and SOOW who is following the route notices and declares the danger ahead (iteration no. 72). The captain perceives and responds to SOOW and orders JOOW for 10 degree starboard rudder command (iteration no. 73). The JOOW responds immediately and executes the rudder order. Hence the grounding is avoided.

On the other hand, when the SOOW decides to utilize large scale chart, the scenario is quite different. As it is shown in Table 10 that the danger is not observed by the SOOW on hischart. However, the Captain who was on the watch himself could look and anticipate the danger and order the JOOW for 10 degree starboard rudder order (iteration no. 172). Yet the decision was not sufficient enough to avoid the scattered rocky zone as shown in Figure 8.

0

500

1000

1500

2000

2500

3000

3500

4000

4500

-1500 -1000 -500 0 500 1000 1500

Adva

nce

Dis

tanc

e (X

in M

eter

)

Transfer Distance (Y in Meter)

Ship path following 'SmallScale Chart'

Ship path following 'LargeScale Chart'

Rocky Zone Visible inSmall Scale Chart

Scattered Rocks

Lteration 1 to 8

Lteration 172

Lteration 72

Lteration 1 to 8

Figure 8. Ship’s path 2 cases: Following small scale chart and following large scale chart.

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Awal A New Approach to Accident Analysis: Multiple Agent Perception-Action 8

Table 9. Results of logical deductions of crew perception-actions (small scale chart chosen for navigation)Ship following 'Small scale chart'

Itera

tion

No.

Cap

tain

Pe

rcep

tion

Cap

tain

A

ctio

n

SOO

W

Perc

eptio

n

SOO

W

Act

ion

JOO

W

Perc

eptio

n

JOO

W

Act

ion

Elap

sed

Tim

esi

nce

voya

ge

star

ted

(sec

)

Adv

ance

D

ista

nce

(m)

Tran

sfer

D

ista

nce

(m)

Hea

ding

(deg

)

1Need to make a sail past

Command SOOW to change voyageplan for sail past

Need to change voyage plan for sail past

2

Need to change voyage plan for sail past

3

Need to conduct

route planning

Conduct route plannin

g on small scale chart

4Ship is

ready for voyage

5Ship is

ready for voyage

Command JOOW

to lift anchor

Need to lift

anchor

6Need to

lift anchor

Lift anchor

7 Anchor lifted

8 Anchor lifted

Command JOOW

- Full Ahead

Need to execute

command - Full Ahead

Execute command

- Full Ahead

9 6 15 0 071 336 1005 0 0

72 Danger ahead

Declare danger ahead

341 1020 0 0

73 Danger ahead

Command JOOW 10 degree starboard

No Action

Need to execute

10 degree starboard

Execute command

- Full Ahead

416 1245 1 0

88 421 1260 1 1

300 1481 4196 1002 42

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Awal A New Approach to Accident Analysis: Multiple Agent Perception-Action 9

Table 10. Results of logical deductions of crew perception-actions (large scale chart chosen for navigation)Ship following 'Large scale chart'

Itera

tion

No.

Cap

tain

Pe

rcep

tion

Cap

tain

A

ctio

n

SOO

W

Perc

eptio

n

SOO

W

Act

ion

JOO

W

Perc

eptio

n

JOO

W

Act

ion

Elap

sed

Tim

esi

nce

voya

ge

star

ted

(sec

)

Adv

ance

D

ista

nce

(m)

Tran

sfer

D

ista

nce

(m)

Hea

ding

(d

eg)

1Need to make a sail past

Command SOOW to

change voyage plan for sail past

Need to change voyage plan for sail past

2

Need to change voyage plan for sail past

3

Need to conduct

route planning

Conduct route

planning on large

scale chart

4Ship is

ready for voyage

5Ship is

ready for voyage

Command JOOW to lift anchor

Need to lift

anchor

6Need to

lift anchor

Lift anchor

7 Anchor lifted

8 Anchor lifted

Command JOOW -

Full Ahead

Need to execute command - Full Ahead

Execute command

- Full Ahead

9 6 15 0 0171 836 2505 0 0

172 Danger ahead

Command JOOW 10

degree starboard

Need to execute

10degree

starboard

Execute 10 degree starboard

841 2520 0 0

178 906 2715 1 0300 1516 4512 285 21

It is visible in Table 9 and Table 10 that out of 300 iterations not all iterations are shown. This is because of two reasons. Firstly, due to limited space. And secondly, not all iterations result in significant change in the simulation. For instance, in Table 10, iteration number 9 to iteration number 171 there is no change in

the perception-action cycle except for the motion of the ship. There for, portraying all the iteration steps are unnecessary.

Anyhow, the iterations shown in the tables above provides a glimpse of the activity that takes place during a voyage.

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Awal A New Approach to Accident Analysis: Multiple Agent Perception-Action 10

Although the results are hypothetical deduction and the knowledge of the agents is very limited, yet the idea presented in this study reveals the complexity of accident analysis. It is needless to mention that with the increase in number to ship crew and intricate natural environment the problem space for accident analysis becomes very difficult and goes beyond human comprehension. Therefore, a computational technique as such could extend the capability of real ship crew and accident analyst as well.

CONCLUSIONThis paper presented a brief history of the development of accident theories and attempted to develop a new methodology for accident analysis. The study proposed application of logic programming domain and agent based concepts to model human perceptions-actions.

It is demonstrated that logical deductions of human perception-action using multiple agents combined with mathematical model of ship maneuvering motions can result in a good instrument for maritime accident analysis. The technique is thereby named logic programming technique (LTP). However, in order to utilize LPT as a risk mitigation tool and apply it in the real world scenario, further elaboration of the concept and its application bearing in mind the practical working arrangements on board ships need to be studied and tested extensively.

This kind of approach to accident problems is very new and appears to have a lot of potentials. Particularly in accident cases where the problem space is very large and complicated, this logic programming technique may become very useful for identifying the root causes and prevention of accidents. In this view the following recommendations are made for the future studies:

• Further development of the methodology and framework for such kind of analysis is necessary.

• Enriching the agent’s knowledge with more perception and action arguments will be realistic.

• Constructing more agents following actual worldscenario will assist dealing with realistic accident problems.

• Utilizing more sophisticated ship maneuvering model where more naturalistic variables can be incorporated,such as wave, wind, drifting of ship, etc.

• And finally, identifying the barriers for practicalapplication of this technique will be very beneficial.

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Programming Technique”, Paper submitted for the Proceedings of the European Safety and Reliability Conference (ESREL), 2015.

Awal, Z.I. and Hasegawa, K. “Analysis of Marine Accidents by Logic Programming Technique”, Proceedings of the International Symposium on Marine Engineering (ISME), Harbin: Paper-ISME127, 2014a.

Awal, Z.I. and Hasegawa, K. “Application of Logic Programming Technique on Maritime Accident Analysis”, Proceedings of the 3rd International Conference on Ship and Offshore Technology (ICSOT),Makassar: 59-66, 2014b.

Awal, Z.I. “A Study on Inland Water Transport Accidents in Bangladesh: Experience of a Decade (1995-2005)”,International Journal for Small Craft Technology (IJSCT), London: 149(B2): 35-42, 2007.

Awal, Z.I., Islam, M.R. and Hoque, M.M. “Collision of Marine Vehicles in Bangladesh: a Study on Accident Characteristics”, Journal of Disaster Prevention and Management, 19(5): 582-595, 2010.

International Maritime Organization (IMO). International Shipping Facts and Figures – Information Resources on Trade, Safety, Security, Environment, London, 2012.

Journée, J.M.J. and Pinkster, J. Introduction in Ship Hydrodynamics, 2002.

Qureshi, Z.H. “A Review of Accident Modelling Approaches for Complex Socio-Technical Systems”, Proceedings of the 12th Australian Conference on Safety-Related Programmable Systems, Adelaide: 47-59, 2007.

Qureshi, Z.H., A Review of Accident Modelling Approaches for Complex Critical Sociotechnical Systems, Adelaide: University of South Australia, 2008.

Rasmussen, J. and Svedung, I. Proactive Risk Management in a Dynamic Society, Karlstad: Swedish Rescue Services Agency, 2000.

Russel, S. and Norvig, P. Artificial Intelligence A Modern Approach (3rd Edition), New Jersey: Prentice Hall,2010.

Stringfellow, M.V. Accident Analysis and Hazard Analysis for Human and Organizational Factors, Massachusetts: Massachusetts Institute of Technology (MIT), 2010.

Tzeng, C.W. and Chen, J.F. “Fundamental Properties of Linear Ship Steering Dynamic Models”, Journal of Marine Science and Technology, 7:2 (1999): 79-88.

Vernez, D., Buchs, D. & Pierrehumbert, G. “Perspectives in the Use of Colored Petri Nets for Risk Analysis and Accident Modelling”, Safety Science 41: 445–463,2003.

Vesely, W.E., Goldberg, F.F., Roberts, N.H., and Haasl, D.F. Fault Tree Handbook, Washington DC: US Nuclear Regulatory Commission, 1981.

Ward, R.B. “Revisiting Heinrich’s law”, Chemeca 2012: Quality of Life through Chemical Engineering,Wellington: 1179-1187, 2012.

Wikipedia. Costa Concordia Disaster, Website: http://en.wikipedia.org/wiki/Costa_Concordia_disaster,2015b.

Wikipedia. List of European Countries by Population, Website: http://en.wikipedia.org/wiki/List_of_European_countries_by_population, 2015a.

Wikipedia. Sinking of the MV Sewol, Website: http://en.wikipedia.org/wiki/Sinking_of_the_MV_Sewol, 2015c.

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Discussion

Fujio Kaneko, National Maritime Research Inst. (V)

The authors’ research on applying logical programing

technique to accident analysis with brief summary of the

history of the development of accident theories

successfully shows that the attempt is considered to be

new and promising method for accident analysis. Table 9

and 10 typically shows that interaction between agents

can be easily pursued with results by the interaction.

Programing for the interaction is mainly declaration of

agents with their roles. Therefore analysis of causes of

the difference of results can be made easy due to the

programing style of Prolog.

The discusser would like to congratulate the authors on

such valuable research.

The discusser is glad if the authors reply to the following

discussion.

1)   The other programing language such as Smalltalk or C++

which are object oriented programing language can also

be used for such purpose. Language style of them are

declaration of agents and methods performed by them.

So why have the authors selected Prolog among those

programing languages? Or, what is the merit of Prolog

in comparison with them?

2)   The example of the paper is too simple to judge the

validity of authors’ attempt. Therefore the authors should

show a prospect that the authors’ attempt will be useful

on more complex real problems besides the future

recommendations in the conclusion.

Authors Response

The authors would like to thank Dr. Kaneko for his

valuable discussion and summarizing the key points of

the concept presented in the paper. One of the important

aspects of such study is to demonstrate the forging of

human aspects with mathematical model in one single

programming domain, which can be very useful in

accident prediction and analysis. Regarding the questions,

which are raised by Dr. Kaneko, the following are the

replies:

1)   The domain of accident problem is complex,

socio-technical, mostly non-numerical and requires

diversified knowledge to explain the problem. In order to

explain an accident or prevent an accident, researchers

identify root causes of accidents and undertake measures

to stop those. This process of identifying root causes is

fundamentally an exercise of logical deductions.

Examples can be seen in various accident reports where

the root causes are identified and preventive measures

are taken to stop future incidents. Therefore, to analyze

accidents more efficiently in a computer program Logic

Programming is preferred in this study. Prolog is one of

the highly recognized logic programming language. The

advantage of Prolog over object oriented programming

language is that Prolog offers very simple programming

syntaxes that are very close to natural language; while

the other programming languages do not have this

characteristic. This unique property of Prolog is suitable

for accident problems where logical deductions are of

main concern. These are just a few notable merits among

many others.

2)   One of the primary focuses of this paper is to establish

the rudimentary concepts of accident analysis by logic

programming. However, the literature review revealed

that no such methods exist, therefore, the basic principles

of this concept needs to be established. The simplest

examples demonstrated in this paper depict the potentials

of the method. Indeed, therefore, elaborated and realistic

studies are the next challenges of this research.

136 A New Approach to Accident Analysis: Multiple Agent Perception-Action

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Ir. M. Rajabalinejad, University of Twente, The

Netherlands (V)

The paper suggests using smart agents for recognition of

faults in the context of a complex system. Given the

rising complexities in products and systems, this is

certainly a direction that academics need to head the

industry to. In this approach smart agents act based on

pre-defined logics. This logic is a model for the

behaviour making that predictable and repeatable. The

interesting approach in this paper is that the logics are

simple and generic.

Although the use of logic in analysis of systems is of

help, yet modelling of human behaviour remains a weak

point for approaches that rely on simplified models of

human. It is hard to find a model that fits all human.

People may act differently based on their training,

experience, mental models, culture, etc. Or people may

act differently under different circumstances like danger

or personal perception.

As shown through accident models like e.g. the Swiss

Cheese model, accidents may happen as a result of a

series of events with low probabilities. Mathematically,

this is of a very low probability that a series of rare

accidents happen together at the same time. The issue is

that there are a lot of those low probability accidents may

happen in the course of system operation. It will be of

great help if the approach can be selective to find the low

probability accidents that are more likely to be ignored

by operators.

In my perspective, the approach used in this paper can

show its full potential on technical systems that adapts

simpler rules for actions. The use of this approach in the

context of intensive human-interaction requires further

development. Human factors remain the main reason for

safety issues. Generalization of rules to model human

action remains a vital challenge for the effective use of

this approach.

Authors Response

The authors thank Dr. Rajabalinejad for his invaluable

remarks regarding this research paper. Dr. Rajabalinejad

has lucidly pointed out the difficulties of modelling

human behaviour and utilization of logic programming

method in low probability accidents. One of the most

important argument of this paper is that human being

despite under various psychological and societal

influence, when under certain responsibility of

indispensable tasks, has to conduct his or her actions

according to certain regulations set forth by the designers

of the task. The actions of ship crew under voyage could

be an example of such kind. It has been observed in

many maritime accidents that the action (seems

legitimate at that instance) taken by ship crew could be

proven wrong or proven as one of the root causes of

accident. The objective of this research is, therefore, to

identify those causes of accidents which are hidden

within a system and which generally seems harmless

until the accident takes place. In such cases, the new

concept presented in this paper may be utilized as a

prudent instrument rather than a tool for gaining

hindsight.

Md. Imran Uddin, Bangladesh University of

Engineering & Technology (V)

It is my great pleasure to discuss this valuable paper. The

paper evidently demonstrated the chronological

development of accident theories. And then it shows a

completely new method of accident analysis. The

methodology and results are easy to understand which

reveal the facts behind an accident; since the chain of

command in a vessel is explained skillfully using logic

programming technique. The authors deserve

congratulations for this pragmatic research work. It

would be a matter of great pleasure if the authors could

further highlight the following points:

1)   The other programming language may be used A New Approach to Accident Analysis: Multiple Agent Perception-Action 137

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to see the results and then it may be interesting

to compare the outcomes.

2)   Question, what may be the probable demerits to

analyze this kind of accident problems in other

programming domains?

3)   Construction of more agents seems pragmatic;

as mentioned in the recommendation of the

paper.

Authors Response

The authors thank the Mr. Uddin for his significant

contributions to this discussion. The following are the

responses based of the comments made by Mr. Uddin:

1)   This study is based on the hypothesis that

accident problems can be analyzed and

solved using logical deductions. This

hypothesis is inferred from the study of

accident theories and analyzing actual

accidents. Therefore, the best way to deal

with such problems will be using

tools/programming techniques that can

perform logical deductions efficiently. In

addition, logic programming has certain

advantages over other programming

domain which seems very useful in solving

accident problems. For example, natural

language handling, shorter codes, dynamic

characteristics and many other advantages

can be mentioned in this regard. It might be

interesting to see how to analyze this kind

of problems in other programming domains.

The authors firmly believe that analysis of

such kind will help to establish the Logic

Programming Technique (LPT) for accident

analysis.

2)   If other programming domains (e.g.

procedural) are employed then several

problems may arise such as: longer coding,

relatively less dynamic in knowledge

handling, complicated logic modelling and

overall inefficiency.

3)   In order to analyze actual systems using

logic programming technique it will be

necessary to construct the logic world as

pragmatic as possible. This includes

applying more agents, more realistic agents

and many other natural parameters. Just

like any other engineering simulation in

procedural or object oriented programming

domain, logic programming technique will

produce more accurate results when more

realistic logic worlds are constructed. It is

the understanding of the authors that such a

domain is still unexplored and the prospects

seem very bright for future research and

development.

138 A New Approach to Accident Analysis: Multiple Agent Perception-Action


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